EEG Artifact Detection and Correction with Deep Autoencoders

TL;DR

This paper introduces LSTEEG, a deep learning autoencoder using LSTM layers for automated detection and correction of artifacts in EEG signals, improving preprocessing accuracy for brain activity analysis.

Contribution

The study presents a novel LSTM-based autoencoder that outperforms existing methods in EEG artifact detection and correction, enhancing automated preprocessing capabilities.

Findings

LSTEEG achieves higher accuracy than convolutional autoencoders.

The model improves interpretability of EEG data.

Automated artifact removal enhances downstream analysis.

Abstract

EEG signals convey important information about brain activity both in healthy and pathological conditions. However, they are inherently noisy, which poses significant challenges for accurate analysis and interpretation. Traditional EEG artifact removal methods, while effective, often require extensive expert intervention. This study presents LSTEEG, a novel LSTM-based autoencoder designed for the detection and correction of artifacts in EEG signals. Leveraging deep learning, particularly LSTM layers, LSTEEG captures non-linear dependencies in sequential EEG data. LSTEEG demonstrates superior performance in both artifact detection and correction tasks compared to other state-of-the-art convolutional autoencoders. Our methodology enhances the interpretability and utility of the autoencoder's latent space, enabling data-driven automated artefact removal in EEG its application in downstream tasks. This research advances the field of efficient and accurate multi-channel EEG preprocessing, and promotes the implementation and usage of automated EEG analysis pipelines for brain health applications.